Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations918
Missing cells1836
Missing cells (%)12.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory293.9 KiB
Average record size in memory327.8 B

Variable types

Numeric8
Unsupported2
Categorical5
Boolean1

Alerts

ChestPainType is highly overall correlated with HeartDiseaseHigh correlation
Cholesterol is highly overall correlated with chol_age_ratio and 1 other fieldsHigh correlation
HeartDisease is highly overall correlated with ChestPainType and 1 other fieldsHigh correlation
RestingBP is highly overall correlated with RestingBP_scaledHigh correlation
RestingBP_scaled is highly overall correlated with RestingBPHigh correlation
ST_Slope is highly overall correlated with HeartDiseaseHigh correlation
chol_age_ratio is highly overall correlated with Cholesterol and 1 other fieldsHigh correlation
chol_age_ratio_scaled is highly overall correlated with Cholesterol and 1 other fieldsHigh correlation
Sex has 918 (100.0%) missing values Missing
Sex_encoded has 918 (100.0%) missing values Missing
Sex is an unsupported type, check if it needs cleaning or further analysis Unsupported
Sex_encoded is an unsupported type, check if it needs cleaning or further analysis Unsupported
Cholesterol has 172 (18.7%) zeros Zeros
Oldpeak has 368 (40.1%) zeros Zeros
chol_age_ratio has 172 (18.7%) zeros Zeros

Reproduction

Analysis started2025-04-27 15:05:11.298243
Analysis finished2025-04-27 15:05:14.303403
Duration3.01 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Age
Real number (ℝ)

Distinct50
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.510893
Minimum28
Maximum77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2025-04-27T10:05:14.337384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile37
Q147
median54
Q360
95-th percentile68
Maximum77
Range49
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.4326165
Coefficient of variation (CV)0.1762747
Kurtosis-0.38613961
Mean53.510893
Median Absolute Deviation (MAD)7
Skewness-0.19593303
Sum49123
Variance88.974254
MonotonicityNot monotonic
2025-04-27T10:05:14.389453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54 51
 
5.6%
58 42
 
4.6%
55 41
 
4.5%
56 38
 
4.1%
57 38
 
4.1%
52 36
 
3.9%
51 35
 
3.8%
59 35
 
3.8%
62 35
 
3.8%
53 33
 
3.6%
Other values (40) 534
58.2%
ValueCountFrequency (%)
28 1
 
0.1%
29 3
 
0.3%
30 1
 
0.1%
31 2
 
0.2%
32 5
0.5%
33 2
 
0.2%
34 7
0.8%
35 11
1.2%
36 6
0.7%
37 11
1.2%
ValueCountFrequency (%)
77 2
 
0.2%
76 2
 
0.2%
75 3
 
0.3%
74 7
0.8%
73 1
 
0.1%
72 4
 
0.4%
71 5
 
0.5%
70 7
0.8%
69 13
1.4%
68 10
1.1%

Sex
Unsupported

Missing  Rejected  Unsupported 

Missing918
Missing (%)100.0%
Memory size7.3 KiB

ChestPainType
Categorical

High correlation 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size46.7 KiB
ASY
496 
NAP
203 
ATA
173 
TA
 
46

Length

Max length3
Median length3
Mean length2.9498911
Min length2

Characters and Unicode

Total characters2708
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowATA
2nd rowNAP
3rd rowATA
4th rowASY
5th rowNAP

Common Values

ValueCountFrequency (%)
ASY 496
54.0%
NAP 203
22.1%
ATA 173
 
18.8%
TA 46
 
5.0%

Length

2025-04-27T10:05:14.433498image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-27T10:05:14.466290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
asy 496
54.0%
nap 203
22.1%
ata 173
 
18.8%
ta 46
 
5.0%

Most occurring characters

ValueCountFrequency (%)
A 1091
40.3%
S 496
18.3%
Y 496
18.3%
T 219
 
8.1%
N 203
 
7.5%
P 203
 
7.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2708
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 1091
40.3%
S 496
18.3%
Y 496
18.3%
T 219
 
8.1%
N 203
 
7.5%
P 203
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2708
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 1091
40.3%
S 496
18.3%
Y 496
18.3%
T 219
 
8.1%
N 203
 
7.5%
P 203
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2708
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 1091
40.3%
S 496
18.3%
Y 496
18.3%
T 219
 
8.1%
N 203
 
7.5%
P 203
 
7.5%

RestingBP
Real number (ℝ)

High correlation 

Distinct67
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean132.39651
Minimum0
Maximum200
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2025-04-27T10:05:14.509547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile106
Q1120
median130
Q3140
95-th percentile160
Maximum200
Range200
Interquartile range (IQR)20

Descriptive statistics

Standard deviation18.514154
Coefficient of variation (CV)0.13983868
Kurtosis3.2712509
Mean132.39651
Median Absolute Deviation (MAD)10
Skewness0.17983931
Sum121540
Variance342.7739
MonotonicityNot monotonic
2025-04-27T10:05:14.562522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120 132
14.4%
130 118
 
12.9%
140 107
 
11.7%
110 58
 
6.3%
150 55
 
6.0%
160 50
 
5.4%
125 29
 
3.2%
135 20
 
2.2%
115 19
 
2.1%
128 18
 
2.0%
Other values (57) 312
34.0%
ValueCountFrequency (%)
0 1
 
0.1%
80 1
 
0.1%
92 1
 
0.1%
94 2
 
0.2%
95 6
 
0.7%
96 1
 
0.1%
98 1
 
0.1%
100 15
1.6%
101 1
 
0.1%
102 3
 
0.3%
ValueCountFrequency (%)
200 4
 
0.4%
192 1
 
0.1%
190 2
 
0.2%
185 1
 
0.1%
180 12
1.3%
178 3
 
0.3%
174 1
 
0.1%
172 2
 
0.2%
170 14
1.5%
165 2
 
0.2%

Cholesterol
Real number (ℝ)

High correlation  Zeros 

Distinct222
Distinct (%)24.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean198.79956
Minimum0
Maximum603
Zeros172
Zeros (%)18.7%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2025-04-27T10:05:14.614702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1173.25
median223
Q3267
95-th percentile331.3
Maximum603
Range603
Interquartile range (IQR)93.75

Descriptive statistics

Standard deviation109.38414
Coefficient of variation (CV)0.55022326
Kurtosis0.11820847
Mean198.79956
Median Absolute Deviation (MAD)46
Skewness-0.61008643
Sum182498
Variance11964.891
MonotonicityNot monotonic
2025-04-27T10:05:14.664310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 172
 
18.7%
254 11
 
1.2%
223 10
 
1.1%
220 10
 
1.1%
230 9
 
1.0%
211 9
 
1.0%
216 9
 
1.0%
204 9
 
1.0%
219 8
 
0.9%
246 8
 
0.9%
Other values (212) 663
72.2%
ValueCountFrequency (%)
0 172
18.7%
85 1
 
0.1%
100 2
 
0.2%
110 1
 
0.1%
113 1
 
0.1%
117 1
 
0.1%
123 1
 
0.1%
126 2
 
0.2%
129 1
 
0.1%
131 1
 
0.1%
ValueCountFrequency (%)
603 1
0.1%
564 1
0.1%
529 1
0.1%
518 1
0.1%
491 1
0.1%
468 1
0.1%
466 1
0.1%
458 1
0.1%
417 1
0.1%
412 1
0.1%

FastingBS
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size45.0 KiB
0
704 
1
214 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters918
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 704
76.7%
1 214
 
23.3%

Length

2025-04-27T10:05:14.709731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-27T10:05:14.734400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 704
76.7%
1 214
 
23.3%

Most occurring characters

ValueCountFrequency (%)
0 704
76.7%
1 214
 
23.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 918
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 704
76.7%
1 214
 
23.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 918
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 704
76.7%
1 214
 
23.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 918
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 704
76.7%
1 214
 
23.3%

RestingECG
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size48.2 KiB
Normal
552 
LVH
188 
ST
178 

Length

Max length6
Median length6
Mean length4.6100218
Min length2

Characters and Unicode

Total characters4232
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNormal
2nd rowNormal
3rd rowST
4th rowNormal
5th rowNormal

Common Values

ValueCountFrequency (%)
Normal 552
60.1%
LVH 188
 
20.5%
ST 178
 
19.4%

Length

2025-04-27T10:05:14.765448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-27T10:05:14.795336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
normal 552
60.1%
lvh 188
 
20.5%
st 178
 
19.4%

Most occurring characters

ValueCountFrequency (%)
N 552
13.0%
o 552
13.0%
r 552
13.0%
m 552
13.0%
a 552
13.0%
l 552
13.0%
L 188
 
4.4%
V 188
 
4.4%
H 188
 
4.4%
S 178
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4232
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 552
13.0%
o 552
13.0%
r 552
13.0%
m 552
13.0%
a 552
13.0%
l 552
13.0%
L 188
 
4.4%
V 188
 
4.4%
H 188
 
4.4%
S 178
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4232
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 552
13.0%
o 552
13.0%
r 552
13.0%
m 552
13.0%
a 552
13.0%
l 552
13.0%
L 188
 
4.4%
V 188
 
4.4%
H 188
 
4.4%
S 178
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4232
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 552
13.0%
o 552
13.0%
r 552
13.0%
m 552
13.0%
a 552
13.0%
l 552
13.0%
L 188
 
4.4%
V 188
 
4.4%
H 188
 
4.4%
S 178
 
4.2%

MaxHR
Real number (ℝ)

Distinct119
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean136.80937
Minimum60
Maximum202
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2025-04-27T10:05:14.885089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile96
Q1120
median138
Q3156
95-th percentile178
Maximum202
Range142
Interquartile range (IQR)36

Descriptive statistics

Standard deviation25.460334
Coefficient of variation (CV)0.18610081
Kurtosis-0.44824782
Mean136.80937
Median Absolute Deviation (MAD)18
Skewness-0.14435942
Sum125591
Variance648.22861
MonotonicityNot monotonic
2025-04-27T10:05:14.937413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150 43
 
4.7%
140 41
 
4.5%
120 36
 
3.9%
130 33
 
3.6%
160 25
 
2.7%
110 23
 
2.5%
125 21
 
2.3%
122 20
 
2.2%
170 20
 
2.2%
115 16
 
1.7%
Other values (109) 640
69.7%
ValueCountFrequency (%)
60 1
0.1%
63 1
0.1%
67 1
0.1%
69 1
0.1%
70 1
0.1%
71 1
0.1%
72 2
0.2%
73 1
0.1%
77 1
0.1%
78 1
0.1%
ValueCountFrequency (%)
202 1
 
0.1%
195 1
 
0.1%
194 1
 
0.1%
192 1
 
0.1%
190 2
0.2%
188 2
0.2%
187 1
 
0.1%
186 2
0.2%
185 4
0.4%
184 4
0.4%
Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
False
547 
True
371 
ValueCountFrequency (%)
False 547
59.6%
True 371
40.4%
2025-04-27T10:05:14.971546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Oldpeak
Real number (ℝ)

Zeros 

Distinct53
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.88736383
Minimum-2.6
Maximum6.2
Zeros368
Zeros (%)40.1%
Negative13
Negative (%)1.4%
Memory size7.3 KiB
2025-04-27T10:05:15.005858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-2.6
5-th percentile0
Q10
median0.6
Q31.5
95-th percentile3
Maximum6.2
Range8.8
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.0665702
Coefficient of variation (CV)1.2019536
Kurtosis1.2030637
Mean0.88736383
Median Absolute Deviation (MAD)0.6
Skewness1.022872
Sum814.6
Variance1.1375719
MonotonicityNot monotonic
2025-04-27T10:05:15.055697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 368
40.1%
1 86
 
9.4%
2 76
 
8.3%
1.5 53
 
5.8%
3 28
 
3.1%
1.2 26
 
2.8%
0.2 22
 
2.4%
0.5 19
 
2.1%
1.4 18
 
2.0%
1.8 17
 
1.9%
Other values (43) 205
22.3%
ValueCountFrequency (%)
-2.6 1
0.1%
-2 1
0.1%
-1.5 1
0.1%
-1.1 1
0.1%
-1 2
0.2%
-0.9 1
0.1%
-0.8 1
0.1%
-0.7 1
0.1%
-0.5 2
0.2%
-0.1 2
0.2%
ValueCountFrequency (%)
6.2 1
 
0.1%
5.6 1
 
0.1%
5 1
 
0.1%
4.4 1
 
0.1%
4.2 2
 
0.2%
4 8
0.9%
3.8 1
 
0.1%
3.7 1
 
0.1%
3.6 4
0.4%
3.5 2
 
0.2%

ST_Slope
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size46.9 KiB
Flat
460 
Up
395 
Down
63 

Length

Max length4
Median length4
Mean length3.1394336
Min length2

Characters and Unicode

Total characters2882
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUp
2nd rowFlat
3rd rowUp
4th rowFlat
5th rowUp

Common Values

ValueCountFrequency (%)
Flat 460
50.1%
Up 395
43.0%
Down 63
 
6.9%

Length

2025-04-27T10:05:15.102639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-27T10:05:15.131559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
flat 460
50.1%
up 395
43.0%
down 63
 
6.9%

Most occurring characters

ValueCountFrequency (%)
F 460
16.0%
l 460
16.0%
a 460
16.0%
t 460
16.0%
U 395
13.7%
p 395
13.7%
D 63
 
2.2%
o 63
 
2.2%
w 63
 
2.2%
n 63
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2882
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
F 460
16.0%
l 460
16.0%
a 460
16.0%
t 460
16.0%
U 395
13.7%
p 395
13.7%
D 63
 
2.2%
o 63
 
2.2%
w 63
 
2.2%
n 63
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2882
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
F 460
16.0%
l 460
16.0%
a 460
16.0%
t 460
16.0%
U 395
13.7%
p 395
13.7%
D 63
 
2.2%
o 63
 
2.2%
w 63
 
2.2%
n 63
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2882
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
F 460
16.0%
l 460
16.0%
a 460
16.0%
t 460
16.0%
U 395
13.7%
p 395
13.7%
D 63
 
2.2%
o 63
 
2.2%
w 63
 
2.2%
n 63
 
2.2%

HeartDisease
Categorical

High correlation 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size45.0 KiB
1
508 
0
410 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters918
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 508
55.3%
0 410
44.7%

Length

2025-04-27T10:05:15.161596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-27T10:05:15.184343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 508
55.3%
0 410
44.7%

Most occurring characters

ValueCountFrequency (%)
1 508
55.3%
0 410
44.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 918
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 508
55.3%
0 410
44.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 918
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 508
55.3%
0 410
44.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 918
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 508
55.3%
0 410
44.7%

Sex_encoded
Unsupported

Missing  Rejected  Unsupported 

Missing918
Missing (%)100.0%
Memory size7.3 KiB

chol_age_ratio
Real number (ℝ)

High correlation  Zeros 

Distinct669
Distinct (%)72.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8051475
Minimum0
Maximum16.030303
Zeros172
Zeros (%)18.7%
Negative0
Negative (%)0.0%
Memory size7.3 KiB
2025-04-27T10:05:15.219659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.051378
median4.1309524
Q35.1039474
95-th percentile6.9392857
Maximum16.030303
Range16.030303
Interquartile range (IQR)2.0525694

Descriptive statistics

Standard deviation2.2325906
Coefficient of variation (CV)0.58672906
Kurtosis0.75216364
Mean3.8051475
Median Absolute Deviation (MAD)1.0240957
Skewness-0.16078431
Sum3493.1254
Variance4.9844609
MonotonicityNot monotonic
2025-04-27T10:05:15.274034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 172
 
18.7%
5 5
 
0.5%
4 5
 
0.5%
4.5 4
 
0.4%
5.952380952 3
 
0.3%
5.613636364 3
 
0.3%
4.095238095 3
 
0.3%
6 3
 
0.3%
4.071428571 3
 
0.3%
4.20754717 2
 
0.2%
Other values (659) 715
77.9%
ValueCountFrequency (%)
0 172
18.7%
1.467532468 1
 
0.1%
1.49122807 1
 
0.1%
1.571428571 1
 
0.1%
1.684931507 1
 
0.1%
1.754385965 1
 
0.1%
1.886792453 1
 
0.1%
2.069444444 1
 
0.1%
2.074626866 1
 
0.1%
2.1 1
 
0.1%
ValueCountFrequency (%)
16.03030303 1
0.1%
11.36585366 1
0.1%
10.96363636 1
0.1%
10.91111111 1
0.1%
9.592592593 1
0.1%
9.56097561 1
0.1%
9.189189189 1
0.1%
9.155555556 1
0.1%
8.766666667 1
0.1%
8.764705882 1
0.1%

RestingBP_scaled
Real number (ℝ)

High correlation 

Distinct67
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0487122 × 10-16
Minimum-7.1549952
Maximum3.6534392
Zeros0
Zeros (%)0.0%
Negative514
Negative (%)56.0%
Memory size7.3 KiB
2025-04-27T10:05:15.325853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-7.1549952
5-th percentile-1.426525
Q1-0.66993455
median-0.12951283
Q30.41090889
95-th percentile1.4917523
Maximum3.6534392
Range10.808434
Interquartile range (IQR)1.0808434

Descriptive statistics

Standard deviation1.0005451
Coefficient of variation (CV)4.8837758 × 1015
Kurtosis3.2712509
Mean2.0487122 × 10-16
Median Absolute Deviation (MAD)0.54042172
Skewness0.17983931
Sum1.8474111 × 10-13
Variance1.0010905
MonotonicityNot monotonic
2025-04-27T10:05:15.374547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.6699345547 132
14.4%
-0.1295128313 118
 
12.9%
0.4109088921 107
 
11.7%
-1.210356278 58
 
6.3%
0.9513306155 55
 
6.0%
1.491752339 50
 
5.4%
-0.399723693 29
 
3.2%
0.1406980304 20
 
2.2%
-0.9401454164 19
 
2.1%
-0.237597176 18
 
2.0%
Other values (57) 312
34.0%
ValueCountFrequency (%)
-7.154995236 1
 
0.1%
-2.831621448 1
 
0.1%
-2.18311538 1
 
0.1%
-2.075031036 2
 
0.2%
-2.020988863 6
 
0.7%
-1.966946691 1
 
0.1%
-1.858862346 1
 
0.1%
-1.750778002 15
1.6%
-1.696735829 1
 
0.1%
-1.642693657 3
 
0.3%
ValueCountFrequency (%)
3.653439233 4
 
0.4%
3.221101854 1
 
0.1%
3.113017509 2
 
0.2%
2.842806647 1
 
0.1%
2.572595786 12
1.3%
2.464511441 3
 
0.3%
2.248342752 1
 
0.1%
2.140258407 2
 
0.2%
2.032174062 14
1.5%
1.761963201 2
 
0.2%

chol_age_ratio_scaled
Real number (ℝ)

High correlation 

Distinct669
Distinct (%)72.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5480234 × 10-16
Minimum-1.7052933
Maximum5.4787561
Zeros0
Zeros (%)0.0%
Negative373
Negative (%)40.6%
Memory size7.3 KiB
2025-04-27T10:05:15.422616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.7052933
5-th percentile-1.7052933
Q1-0.33780507
median0.14601084
Q30.58206274
95-th percentile1.4045775
Maximum5.4787561
Range7.1840493
Interquartile range (IQR)0.9198678

Descriptive statistics

Standard deviation1.0005451
Coefficient of variation (CV)6.463372 × 1015
Kurtosis0.75216364
Mean1.5480234 × 10-16
Median Absolute Deviation (MAD)0.4589529
Skewness-0.16078431
Sum4.8316906 × 10-13
Variance1.0010905
MonotonicityNot monotonic
2025-04-27T10:05:15.478655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.705293261 172
 
18.7%
0.5354782742 5
 
0.5%
0.08732396721 5
 
0.5%
0.3114011207 4
 
0.4%
0.9622918998 3
 
0.3%
0.8104820534 3
 
0.3%
0.1300053298 3
 
0.3%
0.9836325811 3
 
0.3%
0.1193349891 3
 
0.3%
0.1803371253 2
 
0.2%
Other values (659) 715
77.9%
ValueCountFrequency (%)
-1.705293261 172
18.7%
-1.047612265 1
 
0.1%
-1.036992978 1
 
0.1%
-1.001050778 1
 
0.1%
-0.9501839489 1
 
0.1%
-0.9190576344 1
 
0.1%
-0.8597190966 1
 
0.1%
-0.7778628198 1
 
0.1%
-0.7755402954 1
 
0.1%
-0.764169216 1
 
0.1%
ValueCountFrequency (%)
5.478756084 1
0.1%
3.388363009 1
0.1%
3.208107596 1
0.1%
3.184568178 1
0.1%
2.593668425 1
0.1%
2.579499138 1
0.1%
2.412881452 1
0.1%
2.397808394 1
0.1%
2.223526164 1
0.1%
2.22264743 1
0.1%

Interactions

2025-04-27T10:05:13.785526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:11.532390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:11.830409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:12.164495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:12.480538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:12.845692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:13.141587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:13.472946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:13.825202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:11.573896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:11.870702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:12.203104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:12.517441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:12.880258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:13.180133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:13.510152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:13.921129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:11.610442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:11.912405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:12.244522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:12.610609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:12.919612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:13.222342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:13.551487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:13.958143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:11.647509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:11.953778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:12.283546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:12.650155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:12.957575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:13.263427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:13.589158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:14.002055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:11.683647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:11.996445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:12.323647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:12.688426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:12.995560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:13.306681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:13.628347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:14.041080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:11.719393image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:12.036299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:12.361306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:12.725573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:13.028171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:13.345452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:13.663824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:14.083611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:11.758155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:12.080129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:12.401772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:12.766381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:13.067310image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:13.389382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:13.705334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:14.122751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:11.792568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:12.121617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:12.440570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:12.805986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:13.103315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:13.429309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-27T10:05:13.742384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-04-27T10:05:15.532205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AgeChestPainTypeCholesterolExerciseAnginaFastingBSHeartDiseaseMaxHROldpeakRestingBPRestingBP_scaledRestingECGST_Slopechol_age_ratiochol_age_ratio_scaled
Age1.0000.149-0.0470.1950.1800.275-0.3650.2980.2790.2790.1620.190-0.485-0.485
ChestPainType0.1491.0000.1190.4380.1500.5380.2160.1970.0440.0650.0850.2870.1400.140
Cholesterol-0.0470.1191.0000.1030.3220.3290.1840.0520.1090.1090.1500.1190.8500.850
ExerciseAngina0.1950.4380.1031.0000.0470.4910.4100.4720.1520.1250.0970.4550.1320.132
FastingBS0.1800.1500.3220.0471.0000.2630.1020.1660.0930.1200.1200.1700.3360.336
HeartDisease0.2750.5380.3290.4910.2631.0000.4040.4390.1390.1370.0990.6210.3570.357
MaxHR-0.3650.2160.1840.4100.1020.4041.000-0.205-0.108-0.1080.1230.2720.3490.349
Oldpeak0.2980.1970.0520.4720.1660.439-0.2051.0000.1750.1750.1040.419-0.107-0.107
RestingBP0.2790.0440.1090.1520.0930.139-0.1080.1751.0001.0000.0750.113-0.024-0.024
RestingBP_scaled0.2790.0650.1090.1250.1200.137-0.1080.1751.0001.0000.0420.098-0.024-0.024
RestingECG0.1620.0850.1500.0970.1200.0990.1230.1040.0750.0421.0000.0450.1270.127
ST_Slope0.1900.2870.1190.4550.1700.6210.2720.4190.1130.0980.0451.0000.1360.136
chol_age_ratio-0.4850.1400.8500.1320.3360.3570.349-0.107-0.024-0.0240.1270.1361.0001.000
chol_age_ratio_scaled-0.4850.1400.8500.1320.3360.3570.349-0.107-0.024-0.0240.1270.1361.0001.000

Missing values

2025-04-27T10:05:14.192600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-27T10:05:14.262352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AgeSexChestPainTypeRestingBPCholesterolFastingBSRestingECGMaxHRExerciseAnginaOldpeakST_SlopeHeartDiseaseSex_encodedchol_age_ratioRestingBP_scaledchol_age_ratio_scaled
040NaNATA1402890Normal172N0.0Up0NaN7.0487800.4109091.453648
149NaNNAP1601800Normal156N1.0Flat1NaN3.6000001.491752-0.091938
237NaNATA1302830ST98N0.0Up0NaN7.447368-0.1295131.632277
348NaNASY1382140Normal108Y1.5Flat1NaN4.3673470.3028250.251952
454NaNNAP1501950Normal122N0.0Up0NaN3.5454550.951331-0.116383
539NaNNAP1203390Normal170N0.0Up0NaN8.475000-0.6699352.092814
645NaNATA1302370Normal170N0.0Up0NaN5.152174-0.1295130.603676
754NaNATA1102080Normal142N0.0Up0NaN3.781818-1.210356-0.010455
837NaNASY1402070Normal130Y1.5Flat1NaN5.4473680.4109090.735968
948NaNATA1202840Normal120N0.0Up0NaN5.795918-0.6699350.892173
AgeSexChestPainTypeRestingBPCholesterolFastingBSRestingECGMaxHRExerciseAnginaOldpeakST_SlopeHeartDiseaseSex_encodedchol_age_ratioRestingBP_scaledchol_age_ratio_scaled
90863NaNASY1401870LVH144Y4.0Up1NaN2.9218750.410909-0.395842
90963NaNASY1241970Normal136Y0.0Flat1NaN3.078125-0.453766-0.325818
91041NaNATA1201570Normal182N0.0Up0NaN3.738095-0.669935-0.030050
91159NaNASY1641761LVH90N1.0Flat1NaN2.9333331.707921-0.390707
91257NaNASY1402410Normal123Y0.2Flat1NaN4.1551720.4109090.156865
91345NaNTA1102640Normal132N1.2Flat1NaN5.739130-1.2103560.866723
91468NaNASY1441931Normal141N3.4Flat1NaN2.7971010.627078-0.451760
91557NaNASY1301310Normal115Y1.2Flat1NaN2.258621-0.129513-0.693083
91657NaNATA1302360LVH174N0.0Flat1NaN4.068966-0.1295130.118231
91738NaNNAP1381750Normal173N0.0Up0NaN4.4871790.3028250.305656